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FedCLS: Class-Aware Federated Learning in a Heterogeneous Environment

Title
FedCLS: Class-Aware Federated Learning in a Heterogeneous Environment
Author
남해운
Keywords
Federated learning; deep neural networks; heterogeneous network; distributed learning; unbiased aggregation
Issue Date
2023-06-01
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 20, NO 2, Page. 1517-1528
Abstract
Federated learning is an approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users, referred to as clients, are responsible for performing local training using their respective datasets. Once trained, the clients forward their local models to the server, where the models are aggregated to update the global model. Practically, the datasets of clients have different classes of labels regardless of the number of samples. In other words, the data is non-independent and identically distributed (non-iid) among clients in terms of classes of labels, which creates heterogeneity among them. Hence, the local model weights updated by clients result in a broad variation due to heterogeneity among their local datasets. Thus, the process of aggregating the diversified local models of clients has a valuable impact on the performance of global training. When the server aggregates the local models by calculating the weighted average based solely on the number of samples available at the clients, the aggregation process may misguide the global training process. To address this issue, our paper proposes a novel reweighting method called FedCLS that performs based on the volume and variance of local datasets among clients. By taking into account the heterogeneity of data for aggregation in federated learning, the proposed method aims to achieve the minimum global point. The simulation results show that the proposed method achieves 28% performance improvement compared to the conventional federated learning methods.
URI
https://information.hanyang.ac.kr/#/eds/detail?an=edseee.10130085&dbId=edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/190294
ISSN
1932-4537; 2373-7379
DOI
10.1109/TNSM.2023.3278023
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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